

import warnings
from pathlib import Path
from typing import List, Tuple, Union

import fire
from torch import nn

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging


a_      =     logging.get_logger(__name__)

def 			lowerCamelCase__			(       _a				,      _a				,      _a):
	SCREAMING_SNAKE_CASE :   Dict					  =							nn.ModuleList([src_layers[i] for i in layers_to_copy])
	assert len(_a) == len(_a), f"{len(_a)} != {len(_a)}"
	dest_layers.load_state_dict(layers_to_copy.state_dict())


a_      =     {
    # maps  num layers in teacher -> num_layers in student -> which teacher layers to copy.
    # 12: bart, 16: pegasus, 6: marian/Helsinki-NLP
    12: {
        1: [0],  # This says that if the teacher has 12 layers and the student has 1, copy layer 0 of the teacher
        2: [0, 6],
        3: [0, 6, 11],
        4: [0, 4, 8, 11],
        6: [0, 2, 4, 7, 9, 11],
        9: [0, 1, 2, 4, 5, 7, 9, 10, 11],
        12: list(range(12)),
    },
    16: {  # maps  num layers in student -> which teacher layers to copy
        1: [0],
        2: [0, 15],
        3: [0, 8, 15],
        4: [0, 5, 10, 15],
        6: [0, 3, 6, 9, 12, 15],
        8: [0, 2, 4, 6, 8, 10, 12, 15],
        9: [0, 1, 3, 5, 7, 9, 11, 13, 15],
        12: [0, 1, 2, 3, 4, 5, 6, 7, 9, 11, 13, 15],
        16: list(range(16)),
    },
    6: {1: [0], 2: [0, 5], 3: [0, 2, 5], 4: [0, 1, 3, 5], 6: list(range(6))},
}
a_      =     {
    # maps  num layers in student -> which teacher layers to copy.
    6: {1: [5], 2: [3, 5], 3: [1, 4, 5], 4: [1, 2, 4, 5]},
    12: {1: [11], 2: [5, 11], 3: [3, 7, 11], 6: [1, 3, 5, 8, 10, 11]},
    16: {1: [15], 4: [4, 9, 12, 15], 8: [1, 3, 5, 7, 9, 11, 13, 15]},
}

def 			lowerCamelCase__			(       _a				,      _a):
	try:
		SCREAMING_SNAKE_CASE :   Any					  =							LAYERS_TO_COPY[n_teacher][n_student]
		return val
	except KeyError:
		if n_student != n_teacher:
			warnings.warn(
			    f"no hardcoded layers to copy for teacher {n_teacher} -> student {n_student}, defaulting to first"
			    f" {n_student}")
		return list(range(_a))

def 			lowerCamelCase__			(       _a				,      _a):
	if n_student > n_teacher:
		raise ValueError(f"Cannot perform intermediate supervision for student {n_student} > teacher {n_teacher}")
	elif n_teacher == n_student:
		return list(range(_a))
	elif n_student == 1:
		return [n_teacher - 1]
	else:
		return LAYERS_TO_SUPERVISE[n_teacher][n_student]

def 			lowerCamelCase__			(       _a				,      _a = "student"				,      _a = None				,      _a = None				,      _a=False				,      _a=None				,      _a=None				,      **_a				,      ):
	SCREAMING_SNAKE_CASE :   Dict					  =							"encoder_layers and decoder_layers cannot be both None-- you would just have an identical teacher."
	assert (e is not None) or (d is not None), _msg
	if isinstance(_a				,      _a):
		AutoTokenizer.from_pretrained(_a).save_pretrained(_a)  # purely for convenience
		SCREAMING_SNAKE_CASE :   str					  =							AutoModelForSeqaSeqLM.from_pretrained(_a).eval()
	else:
		assert isinstance(_a				,      _a), f"teacher must be a model or string got type {type(_a)}"
	SCREAMING_SNAKE_CASE :   Optional[int]					  =							teacher.config.to_diff_dict()

	try:
		SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   str					  =							teacher.config.encoder_layers, teacher.config.decoder_layers
		if e is None:
			SCREAMING_SNAKE_CASE :   List[str]					  =							teacher_e
		if d is None:
			SCREAMING_SNAKE_CASE :   Optional[Any]					  =							teacher_d
		init_kwargs.update({"encoder_layers": e, "decoder_layers": d})
	except AttributeError:  # T5
		if hasattr(teacher.config				,      "num_encoder_layers"):
			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   str					  =							teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
		else:
			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Tuple					  =							teacher.config.num_layers, teacher.config.num_decoder_layers
		if e is None:
			SCREAMING_SNAKE_CASE :   Optional[Any]					  =							teacher_e
		if d is None:
			SCREAMING_SNAKE_CASE :   List[Any]					  =							teacher_d
		if hasattr(teacher.config				,      "num_encoder_layers"):
			init_kwargs.update({"num_encoder_layers": e, "num_decoder_layers": d})
		else:
			init_kwargs.update({"num_layers": e, "num_decoder_layers": d})

    # Kwargs to instantiate student: teacher kwargs with updated layer numbers + **extra_config_kwargs
	init_kwargs.update(_a)

	# Copy weights
	SCREAMING_SNAKE_CASE :   Tuple					  =							teacher.config_class(**_a)
	SCREAMING_SNAKE_CASE :   Any					  =							AutoModelForSeqaSeqLM.from_config(_a)
	# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
	SCREAMING_SNAKE_CASE :   List[str]					  =							student.load_state_dict(teacher.state_dict()				,      strict=_a)
	assert info.missing_keys == [], info.missing_keys  # every student key should have a teacher keys.

	if copy_first_teacher_layers:  # Our copying is done. We just log and save
		SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Optional[int]					  =							list(range(_a)), list(range(_a))
		logger.info(
		    f"Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to"
		    f" {save_path}")
		student.save_pretrained(_a)
		return student, e_layers_to_copy, d_layers_to_copy

	# Decide which layers of the teacher to copy. Not exactly alternating -- we try to keep first and last layer.
	if e_layers_to_copy is None:
		SCREAMING_SNAKE_CASE :   List[int]					  =							pick_layers_to_copy(_a				,      _a)
	if d_layers_to_copy is None:
		SCREAMING_SNAKE_CASE :   List[int]					  =							pick_layers_to_copy(_a				,      _a)

	try:
		if hasattr(
		    _a				,      "prophetnet"):  # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
			copy_layers(teacher.prophetnet.encoder.layers				,      student.prophetnet.encoder.layers				,      _a)
			copy_layers(teacher.prophetnet.decoder.layers				,      student.prophetnet.decoder.layers				,      _a)
		else:
			copy_layers(teacher.model.encoder.layers				,      student.model.encoder.layers				,      _a)
			copy_layers(teacher.model.decoder.layers				,      student.model.decoder.layers				,      _a)
	except AttributeError:  # For t5, student.model.encoder.layers is called student.encoder.block
		copy_layers(teacher.encoder.block				,      student.encoder.block				,      _a)
		copy_layers(teacher.decoder.block				,      student.decoder.block				,      _a)
	logger.info(
	    f"Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}")
	SCREAMING_SNAKE_CASE :   Optional[int]					  =							{
	    "teacher_type": teacher.config.model_type,
	    "copied_encoder_layers": e_layers_to_copy,
	    "copied_decoder_layers": d_layers_to_copy,
	}
	student.save_pretrained(_a)
	# Save information about copying for easier reproducibility

	return student, e_layers_to_copy, d_layers_to_copy


if __name__ == "__main__":
			fire.Fire(create_student_by_copying_alternating_layers)


from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch

from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch



@require_torch
@require_tf
class        A__(a_							):
						"""simple docstring"""






						def 			UpperCamelCase__					(    self    )     -> List[str]:
									a_						:						List[Any] 							= SMALL_MODEL_IDENTIFIER
									a_						:						Optional[int] 							= """pt"""
									a_						:						Union[str, Any] 							= """tf"""





						def 			UpperCamelCase__					(    self  ,      _lowercase    )     -> str:
									a_						:						Union[str, Any] 							= AutoModel.from_pretrained(self.test_model    )
									model_pt.save_pretrained(_lowercase    )





						def 			UpperCamelCase__					(    self  ,      _lowercase    )     -> List[str]:
									a_						:						int 							= TFAutoModel.from_pretrained(self.test_model  ,      from_pt=_lowercase    )
									model_tf.save_pretrained(_lowercase    )





						def 			UpperCamelCase__					(    self    )     -> Optional[Any]:
									a_						:						str 							= """mock_framework"""

									# Framework provided - return whatever the user provides
									a_						:						Optional[int] 							= FeaturesManager.determine_framework(self.test_model  ,      _lowercase    )
									self.assertEqual(_lowercase  ,      _lowercase    )

									# Local checkpoint and framework provided - return provided framework
									# PyTorch checkpoint
									with TemporaryDirectory() as local_pt_ckpt:
												self._setup_pt_ckpt(_lowercase    )
												a_						:						Optional[int] 							= FeaturesManager.determine_framework(_lowercase  ,      _lowercase    )
												self.assertEqual(_lowercase  ,      _lowercase    )

									# TensorFlow checkpoint
									with TemporaryDirectory() as local_tf_ckpt:
												self._setup_tf_ckpt(_lowercase    )
												a_						:						List[str] 							= FeaturesManager.determine_framework(_lowercase  ,      _lowercase    )
												self.assertEqual(_lowercase  ,      _lowercase    )





						def 			UpperCamelCase__					(    self    )     -> Union[str, Any]:
									# PyTorch checkpoint
									with TemporaryDirectory() as local_pt_ckpt:
												self._setup_pt_ckpt(_lowercase    )
												a_						:						Union[str, Any] 							= FeaturesManager.determine_framework(_lowercase    )
												self.assertEqual(_lowercase  ,      self.framework_pt    )

									# TensorFlow checkpoint
									with TemporaryDirectory() as local_tf_ckpt:
												self._setup_tf_ckpt(_lowercase    )
												a_						:						Union[str, Any] 							= FeaturesManager.determine_framework(_lowercase    )
												self.assertEqual(_lowercase  ,      self.framework_tf    )

									# Invalid local checkpoint
									with TemporaryDirectory() as local_invalid_ckpt:
												with self.assertRaises(_lowercase    ):
															a_						:						Union[str, Any] 							= FeaturesManager.determine_framework(_lowercase    )





						def 			UpperCamelCase__					(    self    )     -> List[Any]:
									a_						:						int 							= MagicMock(return_value=_lowercase    )
									with patch("""transformers.onnx.features.is_tf_available"""  ,      _lowercase    ):
												a_						:						Union[str, Any] 							= FeaturesManager.determine_framework(self.test_model    )
												self.assertEqual(_lowercase  ,      self.framework_pt    )

									# PyTorch not in environment -> use TensorFlow
									a_						:						Dict 							= MagicMock(return_value=_lowercase    )
									with patch("""transformers.onnx.features.is_torch_available"""  ,      _lowercase    ):
												a_						:						Union[str, Any] 							= FeaturesManager.determine_framework(self.test_model    )
												self.assertEqual(_lowercase  ,      self.framework_tf    )

									# Both in environment -> use PyTorch
									a_						:						Optional[Any] 							= MagicMock(return_value=_lowercase    )
									a_						:						List[Any] 							= MagicMock(return_value=_lowercase    )
									with patch("""transformers.onnx.features.is_tf_available"""  ,      _lowercase    ), patch(
									    """transformers.onnx.features.is_torch_available"""  ,      _lowercase    ):
												a_						:						Any 							= FeaturesManager.determine_framework(self.test_model    )
												self.assertEqual(_lowercase  ,      self.framework_pt    )

									# Both not in environment -> raise error
									a_						:						List[str] 							= MagicMock(return_value=_lowercase    )
									a_						:						Optional[Any] 							= MagicMock(return_value=_lowercase    )
									with patch("""transformers.onnx.features.is_tf_available"""  ,      _lowercase    ), patch(
									    """transformers.onnx.features.is_torch_available"""  ,      _lowercase    ):
												with self.assertRaises(_lowercase    ):
															a_						:						Dict 							= FeaturesManager.determine_framework(self.test_model    )


